Bevel Probabilistic Selection Task Performance

Task Information

Total Number of Trials = 104 Sweet taste and bitter taste was selected by participant to reflect reward & punishment

Learning Curve by Shape Pair

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(tidyr)
library(ggplot2)
library(reshape)

## 
## Attaching package: 'reshape'

## The following objects are masked from 'package:tidyr':
## 
##     expand, smiths

## The following object is masked from 'package:dplyr':
## 
##     rename

data <- read.delim("~/Documents/bevel_choice/all_subjects.txt")
names(data) <- c("subj", "run", "pair", "choice", "outcome", "congruent", "RT")

data <- data %>%
  group_by(.dots=c("subj","pair")) %>%
  mutate(Count=row_number())

percent_correct_by_trial_ab <- function(n) {
  count <- sum(data$Count == n & data$choice=="corr" & data$pair == "12")
  countall <- sum(data$Count == n & data$pair == "12")
  return(count/countall)
}

percent_correct_by_trial_cd <- function(n) {
  count <- sum(data$Count == n & data$choice=="corr" & data$pair == "34")
  countall <- sum(data$Count == n & data$pair == "34")
  return(count/countall)
}

percent_correct_by_trial_ef <- function(n) {
  count <- sum(data$Count == n & data$choice=="corr" & data$pair == "56")
  countall <- sum(data$Count == n & data$pair == "56")
  return(count/countall)
}

#percent_correct_by_trial_ab(10)

x <- 1:46
output_ab <- lapply(x, percent_correct_by_trial_ab) 
output_cd <- lapply(x, percent_correct_by_trial_cd) 
output_ef <- lapply(x, percent_correct_by_trial_ef) 

df_ab <- data.frame(matrix(unlist(output_ab), nrow=length(output_ab), byrow=T))
df_cd <- data.frame(matrix(unlist(output_cd), nrow=length(output_cd), byrow=T))
df_ef <- data.frame(matrix(unlist(output_ef), nrow=length(output_ef), byrow=T))

colnames(df_ab)[colnames(df_ab)=="matrix.unlist.output_ab...nrow...length.output_ab...byrow...T."] <- "percent_correct_ab"
colnames(df_cd)[colnames(df_cd)=="matrix.unlist.output_cd...nrow...length.output_cd...byrow...T."] <- "percent_correct_cd"
colnames(df_ef)[colnames(df_ef)=="matrix.unlist.output_ef...nrow...length.output_ef...byrow...T."] <- "percent_correct_ef"

df_ab$trialnum<-row.names(df_ab)
df_cd$trialnum<-row.names(df_cd)
df_ef$trialnum<-row.names(df_ef)
#head(df_ab$trialnum)

data0<-merge(df_ab, df_cd, by="trialnum")
data1<- merge(data0, df_ef, by="trialnum")

df_ab$trial <- seq.int(nrow(df_ab))
df_cd$trial <- seq.int(nrow(df_cd))
df_ef$trial <- seq.int(nrow(df_ef))

Learning Curves for each Shape Pair

plot1 <- ggplot(data=df_ab, aes(x=trial, y=percent_correct_ab, group=1)) +
  geom_line()+
  geom_point() +
  theme_classic() + scale_x_continuous(name="Trial Number") +
  scale_y_continuous(name="Percent of Sample choosing 80% Shape")

plot2 <- ggplot(data=df_cd, aes(x=trial, y=percent_correct_cd, group=1)) +
  geom_line()+
  geom_point() + 
  theme_classic() + scale_x_continuous(name="Trial Number") +
  scale_y_continuous(name="Percent of Sample choosing 70% Shape") 

plot3 <- ggplot(data=df_ef, aes(x=trial, y=percent_correct_ef, group=1)) +
  geom_line()+
  geom_point() + 
  theme_classic() + scale_x_continuous(name="Trial Number") +
  scale_y_continuous(name="Percent of Sample choosing 60% Shape")

plot1

## Warning: Removed 2 rows containing missing values (geom_path).

## Warning: Removed 2 rows containing missing values (geom_point).

plot2

## Warning: Removed 3 rows containing missing values (geom_path).

## Warning: Removed 3 rows containing missing values (geom_point).

plot3

Learning Curves Overlaid

ggplot(data1, aes(as.numeric(trialnum))) + 
  geom_line(aes(y = percent_correct_ab, colour = "80/20 pair")) + 
  geom_line(aes(y = percent_correct_cd, colour = "70/30 pair")) + 
  geom_line(aes(y = percent_correct_ef, colour = "60/40 pair")) +
  theme_classic() + scale_x_continuous(name="Trial Number") +
  scale_y_continuous(name="Percent of Sample choosing Higher %Correct Shape") +
  labs(colour = "Shape Pair")

## Warning: Removed 2 rows containing missing values (geom_path).

## Warning: Removed 3 rows containing missing values (geom_path).

library(ggplot2)
library(ggpubr)

## Warning: package 'ggpubr' was built under R version 3.5.2

## Loading required package: magrittr

## 
## Attaching package: 'magrittr'

## The following object is masked from 'package:tidyr':
## 
##     extract

library(plyr)

## -------------------------------------------------------------------------

## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)

## -------------------------------------------------------------------------

## 
## Attaching package: 'plyr'

## The following object is masked from 'package:ggpubr':
## 
##     mutate

## The following objects are masked from 'package:reshape':
## 
##     rename, round_any

## The following objects are masked from 'package:dplyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──

## ✔ tibble  1.4.2     ✔ purrr   0.2.5
## ✔ readr   1.1.1     ✔ stringr 1.3.1
## ✔ tibble  1.4.2     ✔ forcats 0.3.0

## ── Conflicts ────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ plyr::arrange()     masks dplyr::arrange()
## ✖ purrr::compact()    masks plyr::compact()
## ✖ plyr::count()       masks dplyr::count()
## ✖ reshape::expand()   masks tidyr::expand()
## ✖ magrittr::extract() masks tidyr::extract()
## ✖ plyr::failwith()    masks dplyr::failwith()
## ✖ dplyr::filter()     masks stats::filter()
## ✖ plyr::id()          masks dplyr::id()
## ✖ dplyr::lag()        masks stats::lag()
## ✖ plyr::mutate()      masks ggpubr::mutate(), dplyr::mutate()
## ✖ plyr::rename()      masks reshape::rename(), dplyr::rename()
## ✖ purrr::set_names()  masks magrittr::set_names()
## ✖ plyr::summarise()   masks dplyr::summarise()
## ✖ plyr::summarize()   masks dplyr::summarize()

library(reshape)
library(data.table)

## Warning: package 'data.table' was built under R version 3.5.2

## 
## Attaching package: 'data.table'

## The following object is masked from 'package:purrr':
## 
##     transpose

## The following object is masked from 'package:reshape':
## 
##     melt

## The following objects are masked from 'package:dplyr':
## 
##     between, first, last

Possible outcomes

80-20 == 12 70-30 == 34 60-40 == 56

sub1<-read.table("~/Documents/bevel_choice/by_participant_txtfiles/50.txt", sep="\t", header=F)
head(sub1)

names(sub1)<-c("sub_num","run","type","choice","side","outcome","congruent","RT")
head(sub1)

sub1$side<-as.factor(sub1$side)
sub1$side<-revalue(sub1$side, c("1"="left", "2"="right"))
sub1$trial<-row.names(sub1)
head(sub1)

cbPalette <- c("00900","FF3300")

plot1<-ggplot(subset(sub1, type == "EF"), aes(trial,side, group=as.factor(side)))+
  geom_line()+
  geom_point(aes(color=as.factor(congruent), shape=as.factor(outcome)), size=5)+
  scale_fill_manual(cbPalette)+
  theme_classic()


plot2<-ggplot(subset(sub1, type == "CD"), aes(trial,side, group=as.factor(side)))+
  geom_line()+
  geom_point(aes(color=as.factor(congruent), shape=as.factor(outcome)),  size=5)+
  scale_fill_manual(cbPalette)+
  theme_classic()

plot3<-ggplot(subset(sub1, type == "AB"), aes(trial,side, group=as.factor(side)))+
  geom_line()+
  geom_point(aes(color=as.factor(congruent), shape=as.factor(outcome)),  size=5)+
  geom_point(aes(shape=as.factor(choice)))+
  theme_classic()+
  scale_fill_manual(cbPalette)

ggarrange(plot1,plot2,plot3, 
          labels = c("60-40", "70-30","80-20"),
          ncol = 1, nrow = 3)

plot3<-ggplot(subset(sub1, type == "AB"), aes(as.numeric(trial),side, group=as.factor(side)))+
  geom_line()+
  geom_point(aes(color=as.factor(congruent), shape=as.factor(outcome)),  size=10)+
  geom_point(aes(shape=as.factor(choice), color=as.factor(congruent)), size=5)+
  theme_classic()+
  scale_fill_manual(cbPalette)
plot3

Left right, not informative.

x<-subset(sub1, type == "AB")
summary(as.factor(x$choice))

##    A    B    C    D    E    F Miss 
##   21   18    0    0    0    0    0

summary(x)

##     sub_num      run     type     choice      side      outcome  
##  Min.   :50   run01: 6   AB:39   A   :21   left :27   Miss  : 0  
##  1st Qu.:50   run02:15   CD: 0   B   :18   right:12   punish:23  
##  Median :50   run03: 8   EF: 0   C   : 0   Miss : 0   reward:16  
##  Mean   :50   run04:10           D   : 0                         
##  3rd Qu.:50                      E   : 0                         
##  Max.   :50                      F   : 0                         
##                                  Miss: 0                         
##       congruent         RT        trial          
##  matched   :30   1.567835: 1   Length:39         
##  mismatched: 9   1.581710: 1   Class :character  
##  Miss      : 0   1.583164: 1   Mode  :character  
##                  1.583882: 1                     
##                  1.584442: 1                     
##                  1.584814: 1                     
##                  (Other) :33

data$ov_ob[BMI<18.5]<-“Underweight”

sub1$outcome0[sub1$outcome == "Miss"] <- 0
sub1$outcome0[sub1$outcome == "punish"] <- -10
sub1$outcome0[sub1$outcome == "reward"] <- 10

hm1<-ggplot(sub1,aes(as.numeric(trial), sub_num ,fill=outcome0))+
  geom_tile()+
  scale_fill_gradient2(low="blue", high="red", na.value="black", name="")+
  geom_point(aes(shape=as.factor(choice), size=10, color=as.factor(choice)))
hm1

## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have 7.
## Consider specifying shapes manually if you must have them.

## Warning: Removed 1 rows containing missing values (geom_point).

Super clever function to read in a shit ton of data

readdata <- function(fn){
    dt_temp <- fread(fn, sep="\t")
    return(dt_temp)
}

all.files <- list.files(path = "~/Documents/bevel_choice/by_participant_txtfiles/",pattern = ".txt", full.names = TRUE)
mylist <- lapply(all.files, readdata)
mydata <- rbindlist(mylist, use.names=FALSE)

names(mydata)<-c("sub_num","run","type","choice","side","outcome","congruent","RT")
mydata$side<-as.factor(mydata$side)
mydata$side<-revalue(mydata$side, c("1"="left", "2"="right"))

mydata <- mydata %>%
 group_by(.dots=c("sub_num")) %>%
 dplyr::mutate(Count=row_number())

head(mydata)

mydata$sub_num

##    [1]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##   [24]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##   [47]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##   [70]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##   [93]  1  1  1  1  1  1  1  1  1  1  1  1  2  2  2  2  2  2  2  2  2  2  2
##  [116]  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2
##  [139]  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2
##  [162]  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2
##  [185]  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2
##  [208]  2  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3
##  [231]  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3
##  [254]  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3
##  [277]  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3
##  [300]  3  3  3  3  3  3  3  3  3  3  3  3  4  4  4  4  4  4  4  4  4  4  4
##  [323]  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4
##  [346]  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4
##  [369]  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4
##  [392]  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4
##  [415]  4  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [438]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [461]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [484]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  6  6  6  6  6  6  6  6  6
##  [507]  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6
##  [530]  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6
##  [553]  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6
##  [576]  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6
##  [599]  6  6  6  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7
##  [622]  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7
##  [645]  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7
##  [668]  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7
##  [691]  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  8  8  8  8  8  8  8  8
##  [714]  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8
##  [737]  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8
##  [760]  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8
##  [783]  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8
##  [806]  8  8  8  8  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9
##  [829]  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9
##  [852]  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9
##  [875]  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9
##  [898]  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9 10 10 10 10 10 10 10
##  [921] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [944] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [967] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [990] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [1013] 10 10 10 10 10 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [1036] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [1059] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [1082] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 12 12 12 12 12 12 12 12 12
## [1105] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
## [1128] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
## [1151] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
## [1174] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
## [1197] 12 12 12 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
## [1220] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
## [1243] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
## [1266] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
## [1289] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 14 14 14 14 14 14 14 14
## [1312] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
## [1335] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
## [1358] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
## [1381] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
## [1404] 14 14 14 14 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
## [1427] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
## [1450] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
## [1473] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
## [1496] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 16 16 16 16 16 16 16 16
## [1519] 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16
## [1542] 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16
## [1565] 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16
## [1588] 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16
## [1611] 16 16 16 16 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17
## [1634] 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17
## [1657] 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17
## [1680] 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17
## [1703] 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 18 18 18 18 18 18 18
## [1726] 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18
## [1749] 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18
## [1772] 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18
## [1795] 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18
## [1818] 18 18 18 18 18 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19
## [1841] 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19
## [1864] 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19
## [1887] 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19
## [1910] 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 20 20 20 20 20 20
## [1933] 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20
## [1956] 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20
## [1979] 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20
## [2002] 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20
## [2025] 20 20 20 20 20 20 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21
## [2048] 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21
## [2071] 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21
## [2094] 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21
## [2117] 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 22 22 22 22 22
## [2140] 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22
## [2163] 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22
## [2186] 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22
## [2209] 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22
## [2232] 22 22 22 22 22 22 22 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23
## [2255] 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23
## [2278] 23 23 23 23 23 23 23 23 23 23 23 23 23 24 24 24 24 24 24 24 24 24 24
## [2301] 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24
## [2324] 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24
## [2347] 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24
## [2370] 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24
## [2393] 24 24 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
## [2416] 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
## [2439] 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
## [2462] 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
## [2485] 25 25 25 25 25 25 25 25 25 25 25 25 25 25 26 26 26 26 26 26 26 26 26
## [2508] 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26
## [2531] 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26
## [2554] 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26
## [2577] 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26
## [2600] 26 26 26 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27
## [2623] 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27
## [2646] 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27
## [2669] 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27
## [2692] 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 28 28 28 28 28 28 28 28
## [2715] 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28
## [2738] 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28
## [2761] 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28
## [2784] 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28
## [2807] 28 28 28 28 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29
## [2830] 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29
## [2853] 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29
## [2876] 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29
## [2899] 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 30 30 30 30 30 30 30
## [2922] 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30
## [2945] 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30
## [2968] 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30
## [2991] 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 31 31 31
## [3014] 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31
## [3037] 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31
## [3060] 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31
## [3083] 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31
## [3106] 31 31 31 31 31 31 31 31 31 32 32 32 32 32 32 32 32 32 32 32 32 32 32
## [3129] 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32
## [3152] 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32
## [3175] 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32
## [3198] 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 33 33
## [3221] 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33
## [3244] 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33
## [3267] 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33
## [3290] 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33
## [3313] 33 33 33 33 33 33 33 33 33 33 34 34 34 34 34 34 34 34 34 34 34 34 34
## [3336] 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34
## [3359] 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34
## [3382] 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34
## [3405] 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 35
## [3428] 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35
## [3451] 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35
## [3474] 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35
## [3497] 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35
## [3520] 35 35 35 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36
## [3543] 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36
## [3566] 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36
## [3589] 36 36 36 36 36 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37
## [3612] 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37
## [3635] 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37
## [3658] 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37
## [3681] 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 38 38 38 38 38 38
## [3704] 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38
## [3727] 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38
## [3750] 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38
## [3773] 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38
## [3796] 38 38 38 38 38 38 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39
## [3819] 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39
## [3842] 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39
## [3865] 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39
## [3888] 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 40 40 40 40 40 40 40 40
## [3911] 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40
## [3934] 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40
## [3957] 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40
## [3980] 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40
## [4003] 40 40 40 40 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41
## [4026] 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41
## [4049] 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41
## [4072] 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41
## [4095] 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 42 42 42 42 42 42 42
## [4118] 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42
## [4141] 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42
## [4164] 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42
## [4187] 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42
## [4210] 42 42 42 42 42 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43
## [4233] 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43
## [4256] 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43
## [4279] 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43
## [4302] 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 44 44 44 44 44 44
## [4325] 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44
## [4348] 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44
## [4371] 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44
## [4394] 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44
## [4417] 44 44 44 44 44 44 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45
## [4440] 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45
## [4463] 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45
## [4486] 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45
## [4509] 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 46 46 46 46 46
## [4532] 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46
## [4555] 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46
## [4578] 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46
## [4601] 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46
## [4624] 46 46 46 46 46 46 46 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47
## [4647] 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47
## [4670] 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47
## [4693] 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47
## [4716] 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 48 48 48 48
## [4739] 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48
## [4762] 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48
## [4785] 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48
## [4808] 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48
## [4831] 48 48 48 48 48 48 48 48 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49
## [4854] 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49
## [4877] 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49
## [4900] 49 49 49 49 49 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50
## [4923] 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50
## [4946] 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50
## [4969] 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50
## [4992] 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 51 51 51 51 51 51
## [5015] 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51
## [5038] 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51
## [5061] 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51
## [5084] 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51
## [5107] 51 51 51 51 51 51 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52
## [5130] 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52
## [5153] 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52
## [5176] 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52
## [5199] 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 53 53 53 53 53
## [5222] 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53
## [5245] 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53
## [5268] 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53
## [5291] 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53
## [5314] 53 53 53 53 53 53 53 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54
## [5337] 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54
## [5360] 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54
## [5383] 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54
## [5406] 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 55 55 55 55
## [5429] 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55
## [5452] 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55
## [5475] 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55
## [5498] 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55
## [5521] 55 55 55 55 55 55 55 55 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56
## [5544] 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56
## [5567] 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56
## [5590] 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56
## [5613] 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56 57 57 57
## [5636] 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57
## [5659] 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57
## [5682] 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57
## [5705] 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57
## [5728] 57 57 57 57 57 57 57 57 57 58 58 58 58 58 58 58 58 58 58 58 58 58 58
## [5751] 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58
## [5774] 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58
## [5797] 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58
## [5820] 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 59 59
## [5843] 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59
## [5866] 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59
## [5889] 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59
## [5912] 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59
## [5935] 59 59 59 59 59 59 59 59 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60
## [5958] 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60
## [5981] 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60
## [6004] 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60
## [6027] 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 61 61 61
## [6050] 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61
## [6073] 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61
## [6096] 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61
## [6119] 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61
## [6142] 61 61 61 61 61 61 61 61 61 62 62 62 62 62 62 62 62 62 62 62 62 62 62
## [6165] 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62
## [6188] 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62
## [6211] 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62
## [6234] 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 63 63
## [6257] 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63
## [6280] 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63
## [6303] 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63
## [6326] 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63
## [6349] 63 63 63 63 63 63 63 63 63 63 64 64 64 64 64 64 64 64 64 64 64 64 64
## [6372] 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64
## [6395] 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64
## [6418] 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64
## [6441] 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 65
## [6464] 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65
## [6487] 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65
## [6510] 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65
## [6533] 65 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66
## [6556] 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66
## [6579] 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66
## [6602] 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66
## [6625] 66 66 66 66 66 66 66 66 66 66 66 66 66 67 67 67 67 67 67 67 67 67 67
## [6648] 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67
## [6671] 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67
## [6694] 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67
## [6717] 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67
## [6740] 67 67 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68
## [6763] 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68
## [6786] 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68
## [6809] 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68
## [6832] 68 68 68 68 68 68 68 68 68 68 68 68 68 68 69 69 69 69 69 69 69 69 69
## [6855] 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69
## [6878] 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69
## [6901] 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69
## [6924] 69 69 69 69 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70
## [6947] 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70
## [6970] 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70
## [6993] 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70
## [7016] 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 71 71 71 71 71 71 71
## [7039] 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71
## [7062] 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71
## [7085] 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71
## [7108] 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71
## [7131] 71 71 71 71 71 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72
## [7154] 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72
## [7177] 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72
## [7200] 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72
## [7223] 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 73 73 73 73 73 73
## [7246] 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73
## [7269] 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73
## [7292] 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73
## [7315] 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73
## [7338] 73 73 73 73 73 73 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74
## [7361] 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74
## [7384] 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74
## [7407] 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74
## [7430] 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 75 75 75 75 75
## [7453] 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75
## [7476] 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75
## [7499] 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75
## [7522] 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75
## [7545] 75 75 75 75 75 75 75 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76
## [7568] 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76
## [7591] 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76
## [7614] 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76
## [7637] 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 77 77 77 77
## [7660] 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77
## [7683] 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77
## [7706] 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77
## [7729] 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77
## [7752] 77 77 77 77 77 77 77 77 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78
## [7775] 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78
## [7798] 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78
## [7821] 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78
## [7844] 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 79 79 79
## [7867] 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79
## [7890] 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79
## [7913] 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79
## [7936] 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79
## [7959] 79 79 79 79 79 79 79 79 79 80 80 80 80 80 80 80 80 80 80 80 80 80 80
## [7982] 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80
## [8005] 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80
## [8028] 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80
## [8051] 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 81 81
## [8074] 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81
## [8097] 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81
## [8120] 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81
## [8143] 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81
## [8166] 81 81 81 81 81 81 81 81 81 81 82 82 82 82 82 82 82 82 82 82 82 82 82
## [8189] 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82
## [8212] 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82
## [8235] 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82
## [8258] 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 83
## [8281] 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
## [8304] 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
## [8327] 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
## [8350] 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
## [8373] 83 83 83 83 83 83 83 83 83 83 83 84 84 84 84 84 84 84 84 84 84 84 84
## [8396] 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84
## [8419] 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84
## [8442] 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84
## [8465] 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84
## [8488] 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85
## [8511] 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85
## [8534] 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85
## [8557] 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85
## [8580] 85 85 85 85 85 85 85 85 85 85 85 85 86 86 86 86 86 86 86 86 86 86 86
## [8603] 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86
## [8626] 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86
## [8649] 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86
## [8672] 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 87 87
## [8695] 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87
## [8718] 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87
## [8741] 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87
## [8764] 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87
## [8787] 87 87 87 87 87 87 87 87 87 87 88 88 88 88 88 88 88 88 88 88 88 88 88
## [8810] 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88
## [8833] 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88
## [8856] 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88
## [8879] 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 89
## [8902] 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89
## [8925] 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89
## [8948] 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89
## [8971] 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89
## [8994] 89 89 89 89 89 89 89 89 89 89 89 90 90 90 90 90 90 90 90 90 90 90 90
## [9017] 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90
## [9040] 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90
## [9063] 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90
## [9086] 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90

mydata$outcome0[mydata$outcome == "Miss"] <- 0

## Warning: Unknown or uninitialised column: 'outcome0'.

mydata$outcome0[mydata$outcome == "punish"] <- -10
mydata$outcome0[mydata$outcome == "reward"] <- 10

hmTOTAL<-ggplot(mydata,aes(as.numeric(Count), as.factor(sub_num) ,fill=outcome0))+
  geom_tile()+
  scale_fill_gradient2(low="blue", high="red", na.value="black", name="")+theme_classic()+
  geom_point(aes(shape=as.factor(choice), size=1, color=as.factor(choice)))
hmTOTAL

## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have 7.
## Consider specifying shapes manually if you must have them.

## Warning: Removed 482 rows containing missing values (geom_point).

more_data<-read.table("~/Documents/bevel_choice/clean_bevel.csv",header=T, sep=",")
more_data$sub_num<-row.names(more_data)
head(more_data$sub_num)

## [1] "1" "2" "3" "4" "5" "6"

data0<-merge(mydata, more_data, by="sub_num")
head(data0)

names(data0)

##   [1] "sub_num"                  "run"                     
##   [3] "type"                     "choice"                  
##   [5] "side"                     "outcome"                 
##   [7] "congruent"                "RT"                      
##   [9] "Count"                    "outcome0"                
##  [11] "ID"                       "intials"                 
##  [13] "date"                     "weight"                  
##  [15] "height"                   "BMI"                     
##  [17] "BMI_cat"                  "hba1c"                   
##  [19] "bloodglucose"             "bitter"                  
##  [21] "age"                      "DOB"                     
##  [23] "hispanic"                 "race1"                   
##  [25] "sex"                      "mens_date"               
##  [27] "mens_length"              "sensitivity_reward"      
##  [29] "sensitivity_punish"       "test_result_group"       
##  [31] "preTTfullness"            "preTThunger"             
##  [33] "preTTthirst"              "hourssincelastmeal"      
##  [35] "sweetstim_level"          "sweetstim_pleasent"      
##  [37] "sweetstim_desire"         "sweetstim_intense"       
##  [39] "sweetstim_bitter"         "sweetstim_sweet"         
##  [41] "bitterstim_level"         "bitterstim_pleasent"     
##  [43] "bitterstim_desire"        "bitterstim_intense"      
##  [45] "bitterstim_bitter"        "bitterstim_sweet"        
##  [47] "sweet1pleasent"           "sweet1desire"            
##  [49] "sweet1intense"            "sweet1bitter"            
##  [51] "sweet1sweet"              "sweet2pleasent"          
##  [53] "sweet2desire"             "sweet2intense"           
##  [55] "sweet2bitter"             "sweet2sweet"             
##  [57] "sweet3pleasent"           "sweet3desire"            
##  [59] "sweet3intense"            "sweet3bitter"            
##  [61] "sweet3sweet"              "sweet4pleasent"          
##  [63] "sweet4desire"             "sweet4intense"           
##  [65] "sweet4bitter"             "sweet4sweet"             
##  [67] "sweet1rank"               "sweet2rank"              
##  [69] "sweet3rank"               "sweet4rank"              
##  [71] "bitter1pleasent"          "bitter1desire"           
##  [73] "bitter1intense"           "bitter1bitter"           
##  [75] "bitter1sweet"             "bitter2pleasent"         
##  [77] "bitter2desire"            "bitter2intense"          
##  [79] "bitter2bitter"            "bitter2sweet"            
##  [81] "bitter3pleasent"          "bitter3desire"           
##  [83] "bitter3intense"           "bitter3bitter"           
##  [85] "bitter3sweet"             "bitter4pleasent"         
##  [87] "bitter4desire"            "bitter4intense"          
##  [89] "bitter4bitter"            "bitter4sweet"            
##  [91] "bitter1rank"              "bitter2rank"             
##  [93] "bitter3rank"              "bitter4rank"             
##  [95] "FFQ1"                     "FFQ2"                    
##  [97] "FFQ3"                     "FFQ4"                    
##  [99] "FFQ5"                     "FFQ6"                    
## [101] "FFQ7"                     "FFQ8"                    
## [103] "FFQ9"                     "FFQ10"                   
## [105] "FFQ11"                    "FFQ12"                   
## [107] "FFQ13"                    "FFQ14"                   
## [109] "FFQ15"                    "FFQ16"                   
## [111] "FFQ17"                    "FFQ18"                   
## [113] "FFQ19"                    "FFQ20"                   
## [115] "FFQ21"                    "FFQ22"                   
## [117] "FFQ23"                    "FFQ24"                   
## [119] "FFQ25"                    "FFQ26"                   
## [121] "FFQ27"                    "FFQ28"                   
## [123] "FFQ29"                    "FFQ30"                   
## [125] "FFQ31"                    "FFQ32"                   
## [127] "FFQ33"                    "FFQ34"                   
## [129] "FFQ35"                    "FFQ36"                   
## [131] "FFQ37"                    "FFQ38"                   
## [133] "FFQ39"                    "FFQ40"                   
## [135] "FFQ41"                    "FFQ42"                   
## [137] "FFQ43"                    "FFQ44"                   
## [139] "FFQ45"                    "FFQ46"                   
## [141] "FFQ47"                    "FFQ48"                   
## [143] "FFQ49"                    "FFQ50"                   
## [145] "FFQ51"                    "FFQ52"                   
## [147] "FFQ53"                    "FFQ54"                   
## [149] "FFQ55"                    "FFQ56"                   
## [151] "FFQ57"                    "FFQ58"                   
## [153] "FFQ59"                    "FFQ60"                   
## [155] "BIQ1"                     "BIQ2"                    
## [157] "BIQ3"                     "BIQ4"                    
## [159] "BIQ5"                     "BIQ6"                    
## [161] "BIQ7"                     "BIQ8"                    
## [163] "BIQ9"                     "BIQ10"                   
## [165] "BIQ11"                    "BIQ12"                   
## [167] "BIQ13"                    "BIQ14"                   
## [169] "BIQ15"                    "BIQ16"                   
## [171] "SPSRQ1"                   "SPSRQ2"                  
## [173] "SPSRQ3"                   "SPSRQ4"                  
## [175] "SPSRQ5"                   "SPSRQ6"                  
## [177] "SPSRQ7"                   "SPSRQ8"                  
## [179] "SPSRQ9"                   "SPSRQ10"                 
## [181] "SPSRQ11"                  "SPSRQ12"                 
## [183] "SPSRQ13"                  "SPSRQ14"                 
## [185] "SPSRQ15"                  "SPSRQ16"                 
## [187] "SPSRQ17"                  "SPSRQ18"                 
## [189] "SPSRQ19"                  "SPSRQ20"                 
## [191] "DEBQ1"                    "DEBQ2"                   
## [193] "DEBQ3"                    "DEBQ4"                   
## [195] "DEBQ5"                    "DEBQ6"                   
## [197] "DEBQ7"                    "DEBQ8"                   
## [199] "DEBQ9"                    "DEBQ10"                  
## [201] "DEBQ11"                   "DEBQ12"                  
## [203] "DEBQ13"                   "DEBQ14"                  
## [205] "DEBQ15"                   "DEBQ16"                  
## [207] "DEBQ17"                   "DEBQ18"                  
## [209] "DEBQ19"                   "DEBQ20"                  
## [211] "DEBQ21"                   "DEBQ22"                  
## [213] "DEBQ23"                   "DEBQ24"                  
## [215] "DEBQ25"                   "DEBQ26"                  
## [217] "DEBQ27"                   "DEBQ28"                  
## [219] "DEBQ29"                   "DEBQ30"                  
## [221] "DEBQ31"                   "DEBQ32"                  
## [223] "DEBQ33"                   "BISBAS1"                 
## [225] "BISBAS2"                  "BISBAS3"                 
## [227] "BISBAS4"                  "BISBAS5"                 
## [229] "BISBAS7"                  "BISBAS8"                 
## [231] "BISBAS9"                  "BISBAS10"                
## [233] "BISBAS11"                 "BISBAS12"                
## [235] "BISBAS13"                 "BISBAS14"                
## [237] "BISBAS15"                 "BISBAS16"                
## [239] "BISBAS17"                 "BISBAS18"                
## [241] "BISBAS19"                 "BISBAS20"                
## [243] "BISBAS21"                 "BISBAS22"                
## [245] "BISBAS23"                 "BISBAS24"                
## [247] "IPAQ1"                    "IPAQ2"                   
## [249] "IPAQ3"                    "IPAQ4"                   
## [251] "IPAQ5"                    "IPAQ6"                   
## [253] "IPAQ7"                    "IPAQ8"                   
## [255] "IPAQ9"                    "IPAQ10"                  
## [257] "IPAQ11"                   "IPAQ12"                  
## [259] "IPAQ13"                   "IPAQ14"                  
## [261] "IPAQ15"                   "IPAQ16"                  
## [263] "IPAQ17"                   "IPAQ18"                  
## [265] "IPAQ19"                   "IPAQ20"                  
## [267] "IPAQ21"                   "IPAQ22"                  
## [269] "IPAQ23"                   "IPAQ24"                  
## [271] "IPAQ25"                   "IPAQ26"                  
## [273] "IPAQ27"                   "IPAQ28"                  
## [275] "IPAQ29"                   "IPAQ30"                  
## [277] "IPAQ31"                   "IPAQ32"                  
## [279] "IPAQ33"                   "IPAQ34"                  
## [281] "IPAQ35"                   "IPAQ36"                  
## [283] "IPAQ37"                   "IPAQ38"                  
## [285] "IPAQ39"                   "IPAQ40"                  
## [287] "IPAQ41"                   "IPAQ42"                  
## [289] "IPAQ43"                   "IPAQ44"                  
## [291] "IPAQ45"                   "IPAQ46"                  
## [293] "IPAQ47"                   "IPAQ48"                  
## [295] "IPAQ49"                   "total_MET"               
## [297] "PA_category"              "HAND1"                   
## [299] "HAND2"                    "HAND3"                   
## [301] "HAND4"                    "HAND5"                   
## [303] "HAND6"                    "HAND7"                   
## [305] "HAND8"                    "HAND9"                   
## [307] "HAND10"                   "HAND11"                  
## [309] "HAND12"                   "sleep_qual"              
## [311] "prescanfullness"          "prescanhunger"           
## [313] "prescanthirst"            "SPSRQ_punishment"        
## [315] "SPSRQ_reward"             "BISBAS1_rev"             
## [317] "BISBAS3_rev"              "BISBAS4_rev"             
## [319] "BISBAS5_rev"              "BISBAS7_rev"             
## [321] "BISBAS8_rev"              "BISBAS9_rev"             
## [323] "BISBAS10_rev"             "BISBAS11_rev"            
## [325] "BISBAS12_rev"             "BISBAS13_rev"            
## [327] "BISBAS14_rev"             "BISBAS15_rev"            
## [329] "BISBAS16_rev"             "BISBAS17_rev"            
## [331] "BISBAS18_rev"             "BISBAS19_rev"            
## [333] "BISBAS20_rev"             "BISBAS21_rev"            
## [335] "BISBAS23_rev"             "BISBAS24_rev"            
## [337] "bas_drive"                "bas_funseeking"          
## [339] "bas_rewardresponsiveness" "bas"                     
## [341] "bis"                      "DRES"                    
## [343] "external_eating"          "emotional_eating"        
## [345] "HAND1_new"                "HAND2_new"               
## [347] "HAND3_new"                "HAND4_new"               
## [349] "HAND5_new"                "HAND6_new"               
## [351] "HAND7_new"                "HAND8_new"               
## [353] "HAND9_new"                "HAND10_new"              
## [355] "HAND11_new"               "HAND12_new"              
## [357] "Handedness_score"         "Handedness_cat"          
## [359] "TOTAL_KCAL"               "TOTAL_G_FAT"             
## [361] "TOTAL_G_SUGAR"            "PERC_KCAL_FAT"           
## [363] "PERC_KCAL_SUG"            "PORTIONS_SSB"            
## [365] "nback_accuracy"           "nback_accuracy_SD"       
## [367] "nback_avg_RT"             "nback_avg_RT_SD"         
## [369] "taste_reinforcers"        "reward"                  
## [371] "reward_expected"          "reward_unexpected"       
## [373] "punish"                   "punish_expected"         
## [375] "punish_unexpected"        "RT_run1"                 
## [377] "RT_run2"                  "RT_run3"                 
## [379] "RT_run4"                  "RT_ab"                   
## [381] "RT_cd"                    "RT_ef"                   
## [383] "X._correct"               "X.correct_run1"          
## [385] "X.correct_run2"           "X.correct_run3"          
## [387] "X.correct_run4"           "X.correct_ab_run1"       
## [389] "X.correct_ab_run2"        "X.correct_ab_run3"       
## [391] "X.correct_ab_run4"        "trials_AB_run1"          
## [393] "trials_AB_run2"           "trials_AB_run3"          
## [395] "trials_AB_run4"           "X.correct_cd_run1"       
## [397] "X.correct_cd_run2"        "X.correct_cd_run3"       
## [399] "X.correct_cd_run4"        "trials_cd_run1"          
## [401] "trials_cd_run2"           "trials_cd_run3"          
## [403] "trials_cd_run4"           "X.correct_ef_run1"       
## [405] "X.correct_ef_run2"        "X.correct_ef_run3"       
## [407] "X.correct_ef_run4"        "trials_ef_run1"          
## [409] "trials_ef_run2"           "trials_ef_run3"          
## [411] "trials_ef_run4"           "taste_run1"              
## [413] "taste_run2"               "taste_run3"              
## [415] "taste_run4"               "notes"                   
## [417] "alpha_pos"                "alpha_neg"               
## [419] "criteria_met"             "training_slope"

summary(data0$sensitivity_reward)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.2917  0.4444  0.5000  0.5096  0.5789  0.7500     104

data0$learn[data0$sensitivity_reward < 0.444]<- "didn't learn"
data0$learn[data0$sensitivity_reward >= 0.444 & data0$sensitivity_reward < 0.5  ]<- "meh"
data0$learn[ data0$sensitivity_reward >= 0.5 & data0$sensitivity_reward < 0.57 ]<- "ok"
data0$learn[ data0$sensitivity_reward >= 0.57]<- "pretty pretty good"
summary(as.factor(data0$learn))

##       didn't learn                meh                 ok 
##               2211               1541               2609 
## pretty pretty good               NA's 
##               2643                104

hmTOTALgood<-ggplot(subset(data0, learn == "pretty pretty good"),aes(as.numeric(Count), as.factor(sub_num) ,fill=outcome0))+
  geom_tile()+
  scale_fill_gradient2(low="blue", high="red", na.value="black", name="")+
  geom_point(aes(shape=as.factor(choice), size=1, color=as.factor(choice)))
hmTOTALgood

## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have 7.
## Consider specifying shapes manually if you must have them.

## Warning: Removed 115 rows containing missing values (geom_point).

hmTOTALbad<-ggplot(subset(data0, learn == "didn't learn"),aes(as.numeric(Count), as.factor(sub_num) ,fill=outcome0))+
  geom_tile()+
  scale_fill_gradient2(low="blue", high="red", na.value="black", name="")+
  geom_point(aes(shape=as.factor(choice), size=1, color=as.factor(choice)))
hmTOTALbad

## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have 7.
## Consider specifying shapes manually if you must have them.

## Warning: Removed 116 rows containing missing values (geom_point).

test<-ggarrange(hmTOTALbad,hmTOTALgood, 
          labels = c("bad", "good"),
          ncol = 1, nrow = 2)

## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have 7.
## Consider specifying shapes manually if you must have them.

## Warning: Removed 116 rows containing missing values (geom_point).

## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have 7.
## Consider specifying shapes manually if you must have them.

## Warning: Removed 115 rows containing missing values (geom_point).

data0$choice0[data0$choice == "Miss"] <- 0
# good
data0$choice0[data0$choice == "A"] <- 30
data0$choice0[data0$choice == "C"] <- 20
data0$choice0[data0$choice == "E"] <- 10
# bad
data0$choice0[data0$choice == "B"] <- -30
data0$choice0[data0$choice == "D"] <- -20
data0$choice0[data0$choice == "F"] <- -10

hmTOTALgood_flip<-ggplot(subset(data0, learn == "pretty pretty good"),aes(as.numeric(Count), as.factor(sub_num) ,fill=choice0))+
  geom_tile()+
  scale_fill_gradient2(low="blue", high="red", na.value="black", name="")
  #geom_point(aes(shape=as.factor(outcome), size=1, color=as.factor(outcome)))
hmTOTALgood_flip

hmTOTALbad_flip<-ggplot(subset(data0, learn == "didn't learn"),aes(as.numeric(Count), as.factor(sub_num) ,fill=choice0))+
  geom_tile()+
  scale_fill_gradient2(low="blue", high="red", na.value="black", name="")
  #geom_point(aes(shape=as.factor(outcome), size=1, color=as.factor(outcome)))
hmTOTALbad_flip

test<-ggarrange(hmTOTALbad_flip,hmTOTALgood_flip, 
          labels = c("bad", "good"),
          ncol = 1, nrow = 2)

mytable <- xtabs(~choice+learn, data=data0)
ftable(mytable) # print table 

##        learn didn't learn meh  ok pretty pretty good
## choice                                              
## A                     362 248 417                460
## B                     331 242 420                441
## C                     359 236 401                425
## D                     353 240 413                377
## E                     332 224 410                407
## F                     358 270 384                418
## Miss                  116  81 164                115

summary(mytable) # chi-square test of indepedence

## Call: xtabs(formula = ~choice + learn, data = data0)
## Number of cases in table: 9004 
## Number of factors: 2 
## Test for independence of all factors:
##  Chisq = 23.464, df = 18, p-value = 0.1734

mytable <- xtabs(~outcome+learn, data=data0)
ftable(mytable) # print table 

##         learn didn't learn  meh   ok pretty pretty good
## outcome                                                
## Miss                   116   81  164                115
## punish                1032  717 1218               1227
## reward                1063  743 1227               1301

summary(mytable) # chi-square test of indepedence

## Call: xtabs(formula = ~outcome + learn, data = data0)
## Number of cases in table: 9004 
## Number of factors: 2 
## Test for independence of all factors:
##  Chisq = 10.656, df = 6, p-value = 0.09961

mytable <- xtabs(~congruent+learn, data=data0)
ftable(mytable) # print table 

##            learn didn't learn  meh   ok pretty pretty good
## congruent                                                 
## matched                  1489  997 1734               1777
## mismatched                606  463  711                751
## Miss                      116   81  164                115

summary(mytable) # chi-square test of indepedence

## Call: xtabs(formula = ~congruent + learn, data = data0)
## Number of cases in table: 9004 
## Number of factors: 2 
## Test for independence of all factors:
##  Chisq = 13.713, df = 6, p-value = 0.03301

Ah ha moment

There is a difference between the number of mismatched trials in the “learners” and “non learners.” Those who “don’t learn” have more mismatches.